DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

MPhil Thesis Defence


Title: "DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural 
Networks"

By

Mr. Yuhui DING


Abstract

In this thesis, we propose a novel framework to automatically utilize 
task-dependent semantic information which is encoded in heterogeneous 
information networks (HINs). Specifically, we search for a meta graph, which 
can capture more complex semantic relations than a meta path, to determine how 
graph neural networks (GNNs) propagate messages along different types of edges. 
We formalize the problem within the framework of neural architecture search 
(NAS) and then perform the search in a differentiable manner. We design an 
expressive search space in the form of a directed acyclic graph (DAG) to 
represent candidate meta graphs for a HIN, and we propose task-dependent type 
constraint to filter out those edge types along which message passing has no 
effect on the representations of nodes that are related to the downstream task. 
The size of the search space we define is huge, so we further propose a novel 
and efficient search algorithm to make the total search cost on a par with 
training a single GNN once. Compared with existing popular NAS algorithms, our 
proposed search algorithm improves the search efficiency. We conduct extensive 
experiments on different HINs and downstream tasks to evaluate our method, and 
experimental results show that our method can outperform state-of-the-art 
heterogeneous GNNs and also improves efficiency compared with those methods 
which can implicitly learn meta paths.


Date:  			Wednesday, 11 August 2021

Time:			9:00am - 11:00am

Zoom meeting:
https://hkust.zoom.us/j/95932364164?pwd=Z3BVang2b3loM3FhUnlPaERGbWIxQT09

Committee Members:	Prof. Tong Zhang (Supervisor)
 			Prof. Xiaofang Zhou (Chairperson)
 			Prof. James Kwok


**** ALL are Welcome ****